• Journal of Electrical Engineering and Technology
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• 제3권3호
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• pp.363-372
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• 2008

In this paper, knowledge-based approach using Support Vector Machines (SVMs) are used for estimating the coordinated zonal settings of a distance relay. The approach depends on the detailed simulation studies of apparent impedance loci as seen by distance relay during disturbance, considering various operating conditions including fault resistance. In a distance relay, the impedance loci given at the relay location is obtained from extensive transient stability studies. SVMs are used as a pattern classifier for obtaining distance relay co-ordination. The scheme utilizes the apparent impedance values observed during a fault as inputs. An improved performance with the use of SVMs, keeping the reach when faced with different fault conditions as well as system power flow changes, are illustrated with an equivalent 265 bus system of a practical Indian Western Grid.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume I
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• pp.114-117
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• 2006

One of the most important tasks of ocean color observations is to determine the distribution of phytoplankton primary production. A variety of bio-optical algorithms have been developed estimate primary production from these parameters. In this communication, we investigated the possibility of using a novel universal approximator-support vector machines (SVMs)-as the nonlinear transfer function between oceanic primary production and the information that can be directly retrieved from satellite data. The VGPM (Vertically Generalized Production Model) dataset was used to evaluate the proposed approach. The PPARR2 (Primary Production Algorithm Round Robin 2) dataset was used to further compare the precision between the VGPM model and the SVM model. Using this SVM model to calculate the global ocean primary production, the result is 45.5 PgC $yr^{-1}$, which is a little higher than the VGPM result.

• 한국해양공학회:학술대회논문집
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• 한국해양공학회 2006년 창립20주년기념 정기학술대회 및 국제워크샵
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• pp.46-53
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• 2006

This paper presents the fault diagnosis of induction motor based on support vector machine (SVMs). SVMs are well known as intelligent classifier with strong generalization ability. Application SVMs using kernel function is widely used for multi-class classification procedure. In this paper, the algorithm of SVMs will be combined with feature extraction and reduction using component analysis such as independent component analysis, principal component analysis and their kernel (KICA and KPCA). According to the result, component analysis is very useful to extract the useful features and to reduce the dimensionality of features so that the classification procedure in SVM can perform well. Moreover, this method is used to induction motor for faults detection based on vibration and current signals. The results show that this method can well classify and separate each condition of faults in induction motor based on experimental work.

• Journal of the Korean Data and Information Science Society
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• 제28권6호
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• pp.1271-1278
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• 2017

Although the $L_0$-penalty is the most natural choice to identify the sparsity structure of the model, it has not been widely used due to the computational bottleneck. Recently, the adaptive ridge procedure is developed to efficiently approximate a $L_q$-penalized problem to an iterative $L_2$-penalized one. In this article, we proposed to apply the adaptive ridge procedure to solve the $L_0$-penalized weighted support vector machine (WSVM) to facilitate the corresponding optimization. Our numerical investigation shows the advantageous performance of the $L_0$-penalized WSVM compared to the conventional WSVM with $L_2$ penalty for both simulated and real data sets.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2004년도 추계학술대회
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• pp.179-188
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• 2004

Bankruptcy prediction is an important and widely studied topic since it can have significant impact on bank lending decisions and profitability. Recently, support vector machine (SVM) has been applied to the problem of bankruptcy prediction. The SVM-based method has been compared with other methods such as neural network, logistic regression and has shown good results. Genetic algorithm (GA) has been increasingly applied in conjunction with other AI techniques such as neural network, CBR. However, few studies have dealt with integration of GA and SVM, though there is a great potential for useful applications in this area. This study proposes the methods for improving SVM performance in two aspects: feature subset selection and parameter optimization. GA is used to optimize both feature subset and parameters of SVM simultaneously for bankruptcy prediction.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2010년도 춘계학술대회
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• pp.905-908
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• 2010

본 연구의 목적은 웹 환경에서 스팸 블로그(Splog)를 자동으로 판별하는 시스템을 개발하는 것이다. 먼저 블로그의 HTML을 제거한 후 품사를 부착하였다. 어휘/품사 쌍을 자질로 사용하였으며 카이제곱 통계량을 이용하여 유용한 자질을 선택하였다. 선택된 자질의 가중치를 벡터로 표현한 후, 지지벡터 기계(Support Vector Machines)를 학습하여 자동으로 스팸 블로그를 판별하는 시스템을 제안하였으며, SPLOG 데이터 집합으로 실험한 결과 F1척도로 90.5%의 정확률을 얻었다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2006년도 추계학술대회 학술발표 논문집 제16권 제2호
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• pp.251-254
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• 2006

본 논문은 multiclass 문제에서 기존에 나와 있는 fuzzy support vector mahchines 이 decision boundary 를 설정하는데 있어 모든 훈련 데이터에 대해서 바람직한 decision boundary 를 만들지 못하므로 그러한 경우를 예로 제시한다. 그리고 그에 대한 개선점으로 밀도를 이용해 decision boundary 를 조정하여 기존 FSVM 의 decision boundary 보다 더 타당한 decision boundary 를 설정하는 것을 보인다.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2009년도 춘계학술대회
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• pp.592-595
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• 2009

우리는 지지벡터기계를 이용하여 스팸 이메일을 자동으로 분류하는 시스템을 제안한다. 단어의 어휘 정보와 품사 태그 정보를 지지벡터기계의 자질로 사용한다. 우리는 카이 제곱 통계량을 이용하여 유용한 자질을 선택한 후 각각의 자질을 문서 빈도(TF)와 역문헌빈도(IDF) 값으로 표현하였다. 자질들을 이용하여 SVM을 학습한 후, SVM 분류기는 각각의 이메일의 스팸 유무를 결정한다. 실험 결과, 웹메일 시스템에서 수집한 이메일 데이터에 대해 약 82.7%의 정확률을 얻었다.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제5권11호
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• pp.563-568
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• 2016

단어 의미 분별 문제는 문장에서 어떤 단어가 사전에 가지고 있는 여러 가지 의미 중 정확한 의미를 파악하는 문제이다. 우리는 이 문제를 다중 클래스 분류 문제로 간주하고 지지벡터기계를 이용하여 분류한다. 세종 의미 부착 말뭉치에서 추출한 의미 중의성 단어의 문맥 단어를 두 가지 벡터 공간에 표현한다. 첫 번째는 문맥 단어들로 이뤄진 벡터 공간이고 이진 가중치를 사용한다. 두 번째는 문맥 단어의 윈도우 크기에 따라 문맥 단어를 단어 임베딩 모델로 사상한 벡터 공간이다. 실험결과, 문맥 단어 벡터를 사용하였을 때 약 87.0%, 단어 임베딩을 사용하였을 때 약 86.0%의 정확도를 얻었다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제8권3호
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• pp.196-201
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• 2008

Statistical learning theory has three analytical tools which are support vector machine, support vector regression, and support vector clustering for classification, regression, and clustering respectively. In general, their performances are good because they are constructed by convex optimization. But, there are some problems in the methods. One of the problems is the subjective determination of the parameters for kernel function and regularization by the arts of researchers. Also, the results of the learning machines are depended on the selected parameters. In this paper, we propose an efficient method for objective determination of the parameters of support vector clustering which is the clustering method of statistical learning theory. Using evolutionary algorithm and bootstrap method, we select the parameters of kernel function and regularization constant objectively. To verify improved performances of proposed research, we compare our method with established learning algorithms using the data sets form ucr machine learning repository and synthetic data.